![]() ![]() In Abstractive Summarization, we generate a summary by building an internal semantic representation of the original block of text, and then using this representation to generate an entirely new block of text. Most summarization systems happen to be extractive. The model simply extracts the lines of text that it finds to be important, but does not generate anything that does not already exist in the original block of text. Thus, all of the lines of text in the summary are already present in the original block of text. In Extractive Summarization, we generate a summary by extracting specific lines of text from the original block of text, without altering this text in any way. There are two broad methods that we employ when we summarize text: Text Summarization is essentially the process of shortening a long piece of text (such as an article or an essay) into a summary that conveys the overarching meaning of the text by retaining key information and leaving out the bits that are not important. ![]() In this article, we will learn about the fundamentals of Text Summarization, some of the different ways in which we can summarize text, Transformers, the BART model, and finally, we will practically implement some of these concepts by working with a functioning model (in Python) in order to understand how we can shorten a block of text while retaining all of the important information that it conveys. ![]()
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